Julia Alternatives logo

Julia Alternatives

Explore the pros & cons of Julia and its alternatives. Learn about popular competitors like Python, R Language, and MATLAB
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What is Julia and what are its top alternatives?

Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library.
Julia is a tool in the Package Managers category of a tech stack.
Julia is an open source tool with 44.4K GitHub stars and 5.4K GitHub forks. Here’s a link to Julia's open source repository on GitHub

Top Alternatives to Julia

  • Python
    Python

    Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best. ...

  • R Language
    R Language

    R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible. ...

  • MATLAB
    MATLAB

    Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java. ...

  • Rust
    Rust

    Rust is a systems programming language that combines strong compile-time correctness guarantees with fast performance. It improves upon the ideas of other systems languages like C++ by providing guaranteed memory safety (no crashes, no data races) and complete control over the lifecycle of memory. ...

  • Golang
    Golang

    Go is expressive, concise, clean, and efficient. Its concurrency mechanisms make it easy to write programs that get the most out of multicore and networked machines, while its novel type system enables flexible and modular program construction. Go compiles quickly to machine code yet has the convenience of garbage collection and the power of run-time reflection. It's a fast, statically typed, compiled language that feels like a dynamically typed, interpreted language. ...

  • NumPy
    NumPy

    Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases. ...

  • RubyGems
    RubyGems

    It is a package manager for the Ruby programming language that provides a standard format for distributing Ruby programs and libraries, a tool designed to easily manage the installation of gems, and a server for distributing them. ...

  • Bower
    Bower

    Bower is a package manager for the web. It offers a generic, unopinionated solution to the problem of front-end package management, while exposing the package dependency model via an API that can be consumed by a more opinionated build stack. There are no system wide dependencies, no dependencies are shared between different apps, and the dependency tree is flat. ...

Julia alternatives & related posts

Python logo

Python

238.1K
194.4K
6.8K
A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
238.1K
194.4K
+ 1
6.8K
PROS OF PYTHON
  • 1.2K
    Great libraries
  • 959
    Readable code
  • 844
    Beautiful code
  • 785
    Rapid development
  • 688
    Large community
  • 433
    Open source
  • 391
    Elegant
  • 280
    Great community
  • 272
    Object oriented
  • 218
    Dynamic typing
  • 77
    Great standard library
  • 58
    Very fast
  • 54
    Functional programming
  • 47
    Easy to learn
  • 45
    Scientific computing
  • 35
    Great documentation
  • 28
    Productivity
  • 28
    Matlab alternative
  • 28
    Easy to read
  • 23
    Simple is better than complex
  • 20
    It's the way I think
  • 19
    Imperative
  • 18
    Free
  • 18
    Very programmer and non-programmer friendly
  • 17
    Machine learning support
  • 17
    Powerfull language
  • 16
    Fast and simple
  • 14
    Scripting
  • 12
    Explicit is better than implicit
  • 11
    Ease of development
  • 10
    Clear and easy and powerfull
  • 9
    Unlimited power
  • 8
    It's lean and fun to code
  • 8
    Import antigravity
  • 7
    Python has great libraries for data processing
  • 7
    Print "life is short, use python"
  • 6
    Flat is better than nested
  • 6
    Readability counts
  • 6
    Rapid Prototyping
  • 6
    Fast coding and good for competitions
  • 6
    Now is better than never
  • 6
    There should be one-- and preferably only one --obvious
  • 6
    High Documented language
  • 6
    I love snakes
  • 6
    Although practicality beats purity
  • 6
    Great for tooling
  • 5
    Great for analytics
  • 5
    Lists, tuples, dictionaries
  • 4
    Multiple Inheritence
  • 4
    Complex is better than complicated
  • 4
    Socially engaged community
  • 4
    Easy to learn and use
  • 4
    Simple and easy to learn
  • 4
    Web scraping
  • 4
    Easy to setup and run smooth
  • 4
    Beautiful is better than ugly
  • 4
    Plotting
  • 4
    CG industry needs
  • 3
    No cruft
  • 3
    It is Very easy , simple and will you be love programmi
  • 3
    Many types of collections
  • 3
    If the implementation is easy to explain, it may be a g
  • 3
    If the implementation is hard to explain, it's a bad id
  • 3
    Special cases aren't special enough to break the rules
  • 3
    Pip install everything
  • 3
    List comprehensions
  • 3
    Generators
  • 3
    Import this
  • 2
    Flexible and easy
  • 2
    Batteries included
  • 2
    Can understand easily who are new to programming
  • 2
    Powerful language for AI
  • 2
    Should START with this but not STICK with This
  • 2
    A-to-Z
  • 2
    Because of Netflix
  • 2
    Only one way to do it
  • 2
    Better outcome
  • 2
    Good for hacking
  • 1
    Securit
  • 1
    Slow
  • 1
    Sexy af
  • 0
    Ni
  • 0
    Powerful
CONS OF PYTHON
  • 53
    Still divided between python 2 and python 3
  • 28
    Performance impact
  • 26
    Poor syntax for anonymous functions
  • 22
    GIL
  • 19
    Package management is a mess
  • 14
    Too imperative-oriented
  • 12
    Hard to understand
  • 12
    Dynamic typing
  • 12
    Very slow
  • 8
    Indentations matter a lot
  • 8
    Not everything is expression
  • 7
    Incredibly slow
  • 7
    Explicit self parameter in methods
  • 6
    Requires C functions for dynamic modules
  • 6
    Poor DSL capabilities
  • 6
    No anonymous functions
  • 5
    Fake object-oriented programming
  • 5
    Threading
  • 5
    The "lisp style" whitespaces
  • 5
    Official documentation is unclear.
  • 5
    Hard to obfuscate
  • 5
    Circular import
  • 4
    Lack of Syntax Sugar leads to "the pyramid of doom"
  • 4
    The benevolent-dictator-for-life quit
  • 4
    Not suitable for autocomplete
  • 2
    Meta classes
  • 1
    Training wheels (forced indentation)

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Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 9.5M views

How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

https://eng.uber.com/distributed-tracing/

(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

See more
Nick Parsons
Building cool things on the internet 🛠️ at Stream · | 35 upvotes · 3.3M views

Winds 2.0 is an open source Podcast/RSS reader developed by Stream with a core goal to enable a wide range of developers to contribute.

We chose JavaScript because nearly every developer knows or can, at the very least, read JavaScript. With ES6 and Node.js v10.x.x, it’s become a very capable language. Async/Await is powerful and easy to use (Async/Await vs Promises). Babel allows us to experiment with next-generation JavaScript (features that are not in the official JavaScript spec yet). Yarn allows us to consistently install packages quickly (and is filled with tons of new tricks)

We’re using JavaScript for everything – both front and backend. Most of our team is experienced with Go and Python, so Node was not an obvious choice for this app.

Sure... there will be haters who refuse to acknowledge that there is anything remotely positive about JavaScript (there are even rants on Hacker News about Node.js); however, without writing completely in JavaScript, we would not have seen the results we did.

#FrameworksFullStack #Languages

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R Language logo

R Language

3.2K
1.9K
412
A language and environment for statistical computing and graphics
3.2K
1.9K
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PROS OF R LANGUAGE
  • 84
    Data analysis
  • 63
    Graphics and data visualization
  • 54
    Free
  • 45
    Great community
  • 38
    Flexible statistical analysis toolkit
  • 27
    Easy packages setup
  • 27
    Access to powerful, cutting-edge analytics
  • 18
    Interactive
  • 13
    R Studio IDE
  • 9
    Hacky
  • 7
    Shiny apps
  • 6
    Shiny interactive plots
  • 6
    Preferred Medium
  • 5
    Automated data reports
  • 4
    Cutting-edge machine learning straight from researchers
  • 3
    Machine Learning
  • 2
    Graphical visualization
  • 1
    Flexible Syntax
CONS OF R LANGUAGE
  • 6
    Very messy syntax
  • 4
    Tables must fit in RAM
  • 3
    Arrays indices start with 1
  • 2
    Messy syntax for string concatenation
  • 2
    No push command for vectors/lists
  • 1
    Messy character encoding
  • 0
    Poor syntax for classes
  • 0
    Messy syntax for array/vector combination

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Eric Colson
Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 6.1M views

The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

For more info:

#DataScience #DataStack #Data

See more
Maged Maged Rafaat Kamal
Shared insights
on
PythonPythonR LanguageR Language

I am currently trying to learn R Language for machine learning, I already have a good knowledge of Python. What resources would you recommend to learn from as a beginner in R?

See more
MATLAB logo

MATLAB

1K
688
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A high-level language and interactive environment for numerical computation, visualization, and programming
1K
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PROS OF MATLAB
  • 20
    Simulink
  • 5
    Model based software development
  • 5
    Functions, statements, plots, directory navigation easy
  • 3
    S-Functions
  • 2
    REPL
  • 1
    Simple variabel control
  • 1
    Solve invertible matrix
CONS OF MATLAB
  • 2
    Parameter-value pairs syntax to pass arguments clunky
  • 2
    Doesn't allow unpacking tuples/arguments lists with *
  • 2
    Does not support named function arguments

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Rust logo

Rust

5.6K
4.7K
1.2K
A safe, concurrent, practical language
5.6K
4.7K
+ 1
1.2K
PROS OF RUST
  • 143
    Guaranteed memory safety
  • 130
    Fast
  • 87
    Open source
  • 75
    Minimal runtime
  • 70
    Pattern matching
  • 63
    Type inference
  • 56
    Algebraic data types
  • 56
    Concurrent
  • 46
    Efficient C bindings
  • 43
    Practical
  • 37
    Best advances in languages in 20 years
  • 32
    Safe, fast, easy + friendly community
  • 30
    Fix for C/C++
  • 25
    Stablity
  • 24
    Zero-cost abstractions
  • 23
    Closures
  • 20
    Great community
  • 20
    Extensive compiler checks
  • 18
    No NULL type
  • 18
    Async/await
  • 15
    Completely cross platform: Windows, Linux, Android
  • 15
    No Garbage Collection
  • 14
    Great documentations
  • 14
    High-performance
  • 12
    Super fast
  • 12
    Generics
  • 12
    High performance
  • 11
    Safety no runtime crashes
  • 11
    Guaranteed thread data race safety
  • 11
    Fearless concurrency
  • 11
    Macros
  • 10
    Compiler can generate Webassembly
  • 10
    Helpful compiler
  • 9
    Easy Deployment
  • 9
    RLS provides great IDE support
  • 9
    Prevents data races
  • 8
    Real multithreading
  • 8
    Painless dependency management
  • 7
    Good package management
  • 5
    Support on Other Languages
CONS OF RUST
  • 26
    Hard to learn
  • 23
    Ownership learning curve
  • 11
    Unfriendly, verbose syntax
  • 4
    Variable shadowing
  • 4
    High size of builded executable
  • 4
    Many type operations make it difficult to follow
  • 3
    No jobs
  • 1
    Use it only for timeoass not in production

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Caue Carvalho
Shared insights
on
RustRustGolangGolangPythonPythonRubyRubyC#C#

Hello!

I'm a developer for over 9 years, and most of this time I've been working with C# and it is paying my bills until nowadays. But I'm seeking to learn other languages and expand the possibilities for the next years.

Now the question... I know Ruby is far from dead but is it still worth investing time in learning it? Or would be better to take Python, Golang, or even Rust? Or maybe another language.

Thanks in advance.

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James Cunningham
Operations Engineer at Sentry · | 18 upvotes · 314.1K views
Shared insights
on
PythonPythonRustRust
at

Sentry's event processing pipeline, which is responsible for handling all of the ingested event data that makes it through to our offline task processing, is written primarily in Python.

For particularly intense code paths, like our source map processing pipeline, we have begun re-writing those bits in Rust. Rust’s lack of garbage collection makes it a particularly convenient language for embedding in Python. It allows us to easily build a Python extension where all memory is managed from the Python side (if the Python wrapper gets collected by the Python GC we clean up the Rust object as well).

See more
Golang logo

Golang

22K
13.6K
3.3K
An open source programming language that makes it easy to build simple, reliable, and efficient software
22K
13.6K
+ 1
3.3K
PROS OF GOLANG
  • 548
    High-performance
  • 395
    Simple, minimal syntax
  • 363
    Fun to write
  • 301
    Easy concurrency support via goroutines
  • 273
    Fast compilation times
  • 193
    Goroutines
  • 180
    Statically linked binaries that are simple to deploy
  • 150
    Simple compile build/run procedures
  • 136
    Backed by google
  • 136
    Great community
  • 53
    Garbage collection built-in
  • 45
    Built-in Testing
  • 44
    Excellent tools - gofmt, godoc etc
  • 39
    Elegant and concise like Python, fast like C
  • 37
    Awesome to Develop
  • 26
    Used for Docker
  • 25
    Flexible interface system
  • 24
    Deploy as executable
  • 24
    Great concurrency pattern
  • 20
    Open-source Integration
  • 18
    Easy to read
  • 17
    Fun to write and so many feature out of the box
  • 16
    Go is God
  • 14
    Easy to deploy
  • 14
    Powerful and simple
  • 14
    Its Simple and Heavy duty
  • 13
    Best language for concurrency
  • 13
    Concurrency
  • 11
    Rich standard library
  • 11
    Safe GOTOs
  • 10
    Clean code, high performance
  • 10
    Easy setup
  • 9
    High performance
  • 9
    Simplicity, Concurrency, Performance
  • 8
    Hassle free deployment
  • 8
    Single binary avoids library dependency issues
  • 7
    Gofmt
  • 7
    Cross compiling
  • 7
    Simple, powerful, and great performance
  • 7
    Used by Giants of the industry
  • 6
    Garbage Collection
  • 5
    Very sophisticated syntax
  • 5
    Excellent tooling
  • 5
    WYSIWYG
  • 4
    Keep it simple and stupid
  • 4
    Widely used
  • 4
    Kubernetes written on Go
  • 2
    No generics
  • 1
    Operator goto
  • 1
    Looks not fancy, but promoting pragmatic idioms
CONS OF GOLANG
  • 42
    You waste time in plumbing code catching errors
  • 25
    Verbose
  • 23
    Packages and their path dependencies are braindead
  • 16
    Google's documentations aren't beginer friendly
  • 15
    Dependency management when working on multiple projects
  • 10
    Automatic garbage collection overheads
  • 8
    Uncommon syntax
  • 7
    Type system is lacking (no generics, etc)
  • 5
    Collection framework is lacking (list, set, map)
  • 3
    Best programming language
  • 1
    A failed experiment to combine c and python

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Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 9.5M views

How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

https://eng.uber.com/distributed-tracing/

(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

See more
Nick Parsons
Building cool things on the internet 🛠️ at Stream · | 35 upvotes · 3.3M views

Winds 2.0 is an open source Podcast/RSS reader developed by Stream with a core goal to enable a wide range of developers to contribute.

We chose JavaScript because nearly every developer knows or can, at the very least, read JavaScript. With ES6 and Node.js v10.x.x, it’s become a very capable language. Async/Await is powerful and easy to use (Async/Await vs Promises). Babel allows us to experiment with next-generation JavaScript (features that are not in the official JavaScript spec yet). Yarn allows us to consistently install packages quickly (and is filled with tons of new tricks)

We’re using JavaScript for everything – both front and backend. Most of our team is experienced with Go and Python, so Node was not an obvious choice for this app.

Sure... there will be haters who refuse to acknowledge that there is anything remotely positive about JavaScript (there are even rants on Hacker News about Node.js); however, without writing completely in JavaScript, we would not have seen the results we did.

#FrameworksFullStack #Languages

See more
NumPy logo

NumPy

2.7K
772
14
Fundamental package for scientific computing with Python
2.7K
772
+ 1
14
PROS OF NUMPY
  • 10
    Great for data analysis
  • 4
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CONS OF NUMPY
    Be the first to leave a con

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    Server side

    We decided to use Python for our backend because it is one of the industry standard languages for data analysis and machine learning. It also has a lot of support due to its large user base.

    • Web Server: We chose Flask because we want to keep our machine learning / data analysis and the web server in the same language. Flask is easy to use and we all have experience with it. Postman will be used for creating and testing APIs due to its convenience.

    • Machine Learning: We decided to go with PyTorch for machine learning since it is one of the most popular libraries. It is also known to have an easier learning curve than other popular libraries such as Tensorflow. This is important because our team lacks ML experience and learning the tool as fast as possible would increase productivity.

    • Data Analysis: Some common Python libraries will be used to analyze our data. These include NumPy, Pandas , and matplotlib. These tools combined will help us learn the properties and characteristics of our data. Jupyter notebook will be used to help organize the data analysis process, and improve the code readability.

    Client side

    • UI: We decided to use React for the UI because it helps organize the data and variables of the application into components, making it very convenient to maintain our dashboard. Since React is one of the most popular front end frameworks right now, there will be a lot of support for it as well as a lot of potential new hires that are familiar with the framework. CSS 3 and HTML5 will be used for the basic styling and structure of the web app, as they are the most widely used front end languages.

    • State Management: We decided to use Redux to manage the state of the application since it works naturally to React. Our team also already has experience working with Redux which gave it a slight edge over the other state management libraries.

    • Data Visualization: We decided to use the React-based library Victory to visualize the data. They have very user friendly documentation on their official website which we find easy to learn from.

    Cache

    • Caching: We decided between Redis and memcached because they are two of the most popular open-source cache engines. We ultimately decided to use Redis to improve our web app performance mainly due to the extra functionalities it provides such as fine-tuning cache contents and durability.

    Database

    • Database: We decided to use a NoSQL database over a relational database because of its flexibility from not having a predefined schema. The user behavior analytics has to be flexible since the data we plan to store may change frequently. We decided on MongoDB because it is lightweight and we can easily host the database with MongoDB Atlas . Everyone on our team also has experience working with MongoDB.

    Infrastructure

    • Deployment: We decided to use Heroku over AWS, Azure, Google Cloud because it is free. Although there are advantages to the other cloud services, Heroku makes the most sense to our team because our primary goal is to build an MVP.

    Other Tools

    • Communication Slack will be used as the primary source of communication. It provides all the features needed for basic discussions. In terms of more interactive meetings, Zoom will be used for its video calls and screen sharing capabilities.

    • Source Control The project will be stored on GitHub and all code changes will be done though pull requests. This will help us keep the codebase clean and make it easy to revert changes when we need to.

    See more

    Should I continue learning Django or take this Spring opportunity? I have been coding in python for about 2 years. I am currently learning Django and I am enjoying it. I also have some knowledge of data science libraries (Pandas, NumPy, scikit-learn, PyTorch). I am currently enhancing my web development and software engineering skills and may shift later into data science since I came from a medical background. The issue is that I am offered now a very trustworthy 9 months program teaching Java/Spring. The graduates of this program work directly in well know tech companies. Although I have been planning to continue with my Python, the other opportunity makes me hesitant since it will put me to work in a specific roadmap with deadlines and mentors. I also found on glassdoor that Spring jobs are way more than Django. Should I apply for this program or continue my journey?

    See more
    RubyGems logo

    RubyGems

    6.7K
    6
    0
    Easily download, install, and use ruby software packages on your system
    6.7K
    6
    + 1
    0
    PROS OF RUBYGEMS
      Be the first to leave a pro
      CONS OF RUBYGEMS
        Be the first to leave a con

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        Bower logo

        Bower

        6.4K
        4.5K
        927
        A package manager for the web
        6.4K
        4.5K
        + 1
        927
        PROS OF BOWER
        • 483
          Package management
        • 214
          Open source
        • 142
          Simple
        • 53
          Great for for project dependencies injection
        • 27
          Web components with Meteor
        • 8
          Portable dependencies Management
        CONS OF BOWER
        • 2
          Deprecated
        • 1
          Front end only

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